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Bu çalışmanın amacı, Türkiye ekonomisinde enflasyonun belirleyicilerini ekonometrik olarak araştırmaktır. 2006-2021 dönemini analiz eden çalışmada döviz kuru, faiz oranı, reel para arzı, bütçe açığı ve cari açığın enflasyon oranı üzerindeki etkisi Adımsal (Stepwise) regresyon, Vektör Otoregresyon (VAR) ve Gecikmesi Dağıtılmış Otoregresif (ARDL) model olmak üzere üç farklı zaman serisi tekniği altında tahmin edilmiştir. Elde edilen bulgulara göre, döviz kuru hem kısa hem de uzun dönemde enflasyon oranı üzerinde doğru yönlü etki yaratmaktadır. Reel para arzındaki değişim her iki dönemde de enflasyonu ters yönlü etkilemektedir. Faiz oranı enflasyonu doğru yönlü etkilemektedir. Ancak bu etki marjinal olarak düşüktür. Bütçe açığı kısa dönemde enflasyonu etkilemekle birlikte uzun dönemde enflasyon üzerinde belirleyici değildir. Çalışmada cari açığın enflasyon üzerinde belirleyici olduğuna dair güçlü bulgular elde edilmemiştir. 2006-2021 döneminde ele alınan değişkenler itibariyle Türkiye ekonomisinde uzun dönemde enflasyon üzerinde istatistiksel olarak en etkili faktör döviz kurundaki değişimlerdir.

This study examines the industry-specific effects of productivity shocks on exports and the internationalisation of the largest Croatian exporters. In order to answer two research questions: (1) Which hypothesis, the productivity-led hypothesis or export-led hypothesis, holds in the case of the largest Croatian exporters? (2) Are the effects of productivity shocks on exports and internationalization sectoral dependent, and in what way? The authors tested 300 largest exporters’ micro- financial data for the 2006-2015 period by using a vector autoregression (VAR) method. Three productivity measures examined are total factor productivity, labour productivity and capital productivity. The results imply that productivity-led hypothesis holds for majority of Croatian largest exporters’ sectors. Rather than a specific export-led hypothesis, a bi-directional flow has proved to have greater influence on several industrial sectors, including professional and scientific services and administrative services sectors, and to a lesser extent, transport and warehousing, accommodation and food sectors. It is predominantly negative in terms of TFP and positive in terms of labour productivity (agriculture, electricity and gas supply, wholesale and transport and warehousing, and information and communication) and capital productivity (electricity and gas supply). Managerial and policy implications of productivity shocks are discussed in the paper.

Abstract Background Self-rated health among old adults (SHOA) indicates individuals' subjective assessments and evaluations of their overall health based on objective physical circumstances. The purpose of this study was to analyze the current state and influencing factors of the subjective perception-based self-rated health (SH) by qualifying selected older adults with similar objective physical conditions, as well as to explore the equality and changing trends of SHOA based on influencing factors. Methods This study designed a cross-sectional study, conducted in three provinces in east, central and west China, and included 1,153 older adults (> = 60 years) with intact physical condition (IPC). The current state of SHOA and its influencing factors were analyzed using mean comparisons and Logistic regression (LR) models. The equality level and trend of SHOA's effect on health literacy, health habits, and access to health care were determined using the Lorenz curve, Gini coefficient, and Vector Autoregression (VAR) model. Results The mean SHOA with IPC was 74.37 ± 13.22. Findings from LR modeling indicated that SHOA with IPC was mainly influenced by age and communication methods (P

This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility.

The half-normal distribution is composited with the Pareto model to obtain a uni-parametric distribution with a heavy right tail, called the composite half-normal-Pareto distribution. This new distribution is useful for modeling positive data with atypical observations. We study the properties and the behavior of the right tail of this new distribution. We estimate the parameter using a method based on percentiles and the maximum likelihood method and assess the performance of the maximum likelihood estimator using Monte Carlo. We report three applications, one with simulated data and the others with income and expenditure data, in which the new distribution presents better performance than the Pareto distribution.

The implementation of Video Assistant Referees (VARs) in 2018 has had a significant impact on the multi-billion-dollar soccer industry. As the most popular and watched sport globally, soccer’s financial stakes are high, with clubs, leagues, broadcasters, sponsors, and fans heavily invested in the game. The ongoing debate surrounding the VAR system brings to light the intricate balance between preserving the authenticity of football (soccer) and harnessing technology to improve accuracy. It is crucial to strike the right equilibrium in order to uphold football’s metaphorical power and sustain the timeless joy it has brought to fans throughout generations. In this context, Immanuel Kant’s philosophy can offer valuable insights into the utilization of VARs in soccer. According to Kantian ethics, using VARs can be justified if it serves to enhance fairness and accuracy, aligning with the moral duties of referees. Nevertheless, it is important to consider the potential dehumanizing effects and the necessity of preserving the value of human judgment in the game. Therefore, this paper aims to explore in-depth the intricate dynamics that arise when technology is integrated into traditional practices, emphasizing the significance of critical reflection on the implications of such advancements.

The research attempts to delve further into the relationship of oil volatility and economic growth of top oil exporting and importing countries. Annual time series data on oil prices and economic growth (1987-2022) has been considered for top 5 exporting and importing countries. Basic statistical techniques and VAR regressions have been used to analyze data. The relationship between volatility and economic activity was found to be more significant for exporting countries rather than importing countries and a lag effect on 6 years is observed as optimal in this relationship. The global financial crisis was observed as an insignificant event on oil volatility (contrary to Ftiti et al.,2016). One of the significant finding of the study is that Japan’s economic growth is positively associated with the long term oil price volatility.

• Mental Health Concerns (MHC) index is constructed using Google Trends data. • We study if economic uncertainty raises concerns about mental health in the U.S. • A positive shock to uncertainty is shown to raise MHC index. This paper investigates if heightened economic uncertainty raises concerns about mental health in the U.S. We first quantify such concerns by constructing a composite Mental Health Concerns index, using time series of the intensity of Google search queries related to mental disorders and distress. This index i) rises significantly during the three recessionary episodes and ii) comoves negatively with survey responses that reflect views on current consumer sentiment or on future economic conditions. We find that the concerns regarding mental health substantially increase after an unexpected hike in economic uncertainty; uncertainty not only channels through its negative impacts on economic activity, but also directly affects the level of concerns. Our findings suggest that an uncertainty shock can have a far-reaching impact on overall welfare of economic agents by leaving them more concerned about mental health. [ABSTRACT FROM AUTHOR]

This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric models for ten Asian markets using daily data for the time period from 12 September 1994 to 05 March 2018. The empirical results indicate that ML algorithms, across the range of countries, can better approximate dependencies compared to traditional benchmark models. Notably, the predictive performance of such deep learning models is superior perhaps due to its ability in capturing long-range dependencies. For example, the Neuro Fuzzy models of ANFIS and CANFIS, which outperform the EGARCH model, are more flexible in modelling both asymmetry and long memory properties. This offers new insights for Asian markets. In addition to standard statistics forecast metrics, we also consider risk management measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the expected shortfall (ES) and the dynamic quantile test. The study concludes that ML algorithms provide improving volatility forecasts in the stock markets of Asia and suggest that this may be a fruitful approach for risk management. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Economic & Social Research is the property of Abant Izzet Baysal University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekonomik Yorumlar is the property of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekomomik Yorumlar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The central role of Value-at-Risk (VaR) within bank market risk regulation received significant criticism from financial media and government investigations into the events of the 2007–2009 financial crisis. Impending reform of bank market risk regulation under the Fundamental Review of the Trading Book (FRTB) demotes VaR, replacing it with a layered framework centred on expected shortfall (ES). However, many of these criticisms assume full integration of internal and regulatory market risk models and further, a linear relationship between risk models and regulatory capital. We examine bank practitioners' perspectives and experienced realities to better understand the operational relationship between internal and regulatory market risk models, and between risk models and capital. This has important policy implications for the efficacy of the reforms to banking regulation, financial stability and navigating the dichotomy of private and public interests. [ABSTRACT FROM AUTHOR]

The automobile is an important part of transportation systems. Accurate prediction of sales prospects of different power vehicles can provide an important reference for national scientific decision making, flexible operation of enterprises and rational purchases of consumers. Considering that China has achieved the goal of 20% sales of new energy vehicles ahead of schedule in 2025, in order to accurately judge the competition pattern of new and old kinetic energy vehicles in the future, the automobile market is divided into three types according to power types: traditional fuel vehicles, new energy vehicles and plug-in hybrid vehicles. Based on the monthly sales data of automobiles from March 2016 to March 2023, the prediction effects of multiple models are compared from the perspective of univariate prediction. Secondly, based on the perspective of multivariate prediction, combined with the data of economic, social and technical factors, a multivariate prediction model with high prediction accuracy is selected. On this basis, the sales volume of various power vehicles from April 2023 to December 2025 is predicted. Univariate prediction results show that in 2025, the penetration rates of three types of vehicles will reach 43.8%, 44.4% and 11.8%, respectively, and multivariate prediction results show that the penetration rates will reach 51.0%, 37.9% and 11.1%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a new risk measurement model that directly incorporate information from high‐frequency data to predict daily Value‐at‐Risk and expected shortfall. In this model, Regular‐Vine copula and Monte Carlo simulation are applied to produce the predicted intraday returns that have nonlinear dependences. And the time‐varying marginal distribution of intraday returns is estimated under the framework of generalized autoregressive score. The model is so named R‐Vine‐Copula‐GAS (abbreviated as RVCGAS). The predicted intraday returns in the same day are added up to obtain the daily return and form its empirical distribution. The risk measurements are then calculated based on this simulated distribution. An empirical analysis is conducted using data from the index in China's stock market and some of its constituents, and the effects of the proposed model and several Benchmark models are compared through some backtestings. The results show that RVCGAS has an advantage over others in predicting tail risk when the risk level is high, since it could cover more risky returns and reduce more costs. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic, which emerged in December 2019 in China for the first time, affected the whole world in a short while. The pandemic spread to all countries not only damaged human health but also led to the losses in the global economy as the restrictions placed on struggling with the outbreak have resulted in shrinking the world economy. In the meantime, different sectors have been affected differently by the economic situation in line with their own characteristics. In this context, the aim of the present study was to examine the effects of the Covid-19 pandemic crisis on the Food, Service, Technology, Wholesale-Retail Trade, Electricity, and Transportation sectors in the Borsa Istanbul (BIST) indice using time series analysis. In this direction, the impulse variable in the analysis consisted of the number of daily active cases for the period 17.03.2020-31.05.2022, and the response variables consisted of the BIST sector indices. To analyze the relationship between the variables, the ADF and Zivot-Andrews unit root tests were used and it was determined that the variables were stationary at the level. In order to determine the causality relationship between the variables, we ran the Granger Causality test based on the Vector Autoregressive (VAR) model. The findings revealed that there is a unidirectional Granger Causality relationship from the number of active cases to the Food, Service, Technology, Wholesale-Retail Trade, Electricity, and Transportation sectors, and also that these sectors respond differently to the change in the number of active cases. [ABSTRACT FROM AUTHOR]

This paper studies the effects of a negative economic shock on short- and long-term youth emancipation in Spain over the period 1995-2017. We use a vector autoregressive (VAR) model with different endogenous and exogenous variables which might have an impact on youth residential emancipation according to the academic literature. The results show how emancipation is impacted negatively by the shock after two quarters on average. Following this, the situation returns to its prior state at an accelerated rate. We also find that, in the short-term, the unemployment rate has a greater influence than the temporary employment rate on youth emancipation. In the long-term, this trend is reversed. To conclude, we find that emancipation processes do not depend as much on entry into the labour market as they do on the conditions to stay in it. [ABSTRACT FROM AUTHOR]

Copyright of Lecturas de Economia is the property of Universidad de Antioquia, Facultad de Ciencias Economicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we use a copula to examine the relationship and dynamic dependence structure between the crude oil market and the BRICS countries' stock indices expressed through financial crises, from the 2008 global financial crisis to COVID-19, based on daily data. We characterize the long-term relationship as well as the short-term dynamics and represent the interdependence between them. We also study the short-run conditional links through the considered variables under the effects of long-run interactions and the asymmetric volatility spillover relationship. In addition, we establish that the volatility transmission is stubborn and that the impact of the crises and our empirical findings prove that there is fractional co-integration between crude oil and financial markets. We notice that there are lengthy correlations between the variables, as we detect significant bidirectional causal links. In particular, we see positive short-run links and use an optimal copula coefficient to measure the risk spillovers between oil markets and financial markets that represent the dependence structure. For robustness purposes, based on a sliding-window analysis, we complement our investigation with VaR analysis. [ABSTRACT FROM AUTHOR]

Determining accurate capital requirements is a central activity across the life insurance industry. This is computationally challenging and often involves the acceptance of proxy errors that directly impact capital requirements. Within simulation-based capital models, where proxies are being used, capital estimates are approximations that contain both statistical and proxy errors. Here, we show how basic error analysis combined with targeted exact computation can entirely eliminate proxy errors from the capital estimate. Consideration of the possible ordering of losses, combined with knowledge of their error bounds, identifies an important subset of scenarios. When these scenarios are calculated exactly, the resulting capital estimate can be made devoid of proxy errors. Advances in the handling of proxy errors improve the accuracy of capital requirements. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi is the property of Journal of Economic Policy Researches / Iktisat Politikasi Arastirmalari Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studies the impact of investor sentiment on the Bitcoin returns and conditional volatility taking into account the Covid-19 outbreak by using different investor sentiment proxies and by employing the EGARCH model. Estimation results show that investor sentiment has a positive impact on the Bitcoin returns and their volatility, especially after the Covid-19 outbreak. The VAR model is employed to investigate whether investor sentiment and Bitcoin returns are related in a dynamic setting and to make distinguish between rational and irrational investor sentiments. The results from the VAR model show that both rational and irrational investor sentiments have an impact on Bitcoin returns indicating that the Bitcoin market is also driven by emotions and noise traders have an impact on the data generating process of Bitcoin returns. The positive impact of investor sentiment can be attributed to the fear of missing out (FOMO) behavior of speculative and irrational investors. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of −0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss. [ABSTRACT FROM AUTHOR]

This article assesses the impact of uncertainties on the effectiveness of monetary policy in Brazil after the adoption of the inflation-targeting regime. We employ the methodology of autoregressive vectors with an endogenous threshold (TVAR) with a general uncertainty indicator (IGI), proposed as a linear combination of four existing proxies for the Brazilian context. The sample covers 2003 to June 2022 at a monthly frequency. The results show the IGI variable has the highest degree of correlation with economic recessions in the country among all the analysed indicators. Moreover, in regimes of high uncertainty, the responses of the output gap, inflation, and inflationary expectations to interest rate shocks are severely reduced. Therefore, we conclude that an increase in macroeconomic uncertainties can reduce the effectiveness of monetary policy in Brazil. [ABSTRACT FROM AUTHOR]

This study employs an augmented realized GARCH (RGARCH) model to examine whether two well‐known tail risk measures, namely the SKEW and VVIX indices, can improve the daily value‐at‐risk (VaR) forecasting accuracy for S&P500 index returns. We find that the RGARCH‐VVIX model exhibits better predictive accuracy than the RGARCH and RGARCH‐SKEW models. The VVIX index provides economically valuable information in forecasting VaR. Given its ability to improve both accuracy and efficiency for VaR forecasts, the RGARCH‐VVIX model is helpful for a risk manager to determine capital requirement and for investors to assess the downside risk of their investments. [ABSTRACT FROM AUTHOR]

Copyright of Strategy & Development Review is the property of Strategy & Development Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study analyzes the impact of Covid-19 on stock market liquidity of China and four worst hit countries by the pandemic. Using daily data for the stock market illiquidity spanning over July 1, 2019 to July 10, 2020 and the data for new cases and deaths over the period from December 31, 2019 to July 10, 2020, the results of our GARCH analysis show that liquidity in stock markets of all the sampled countries hit hard by the news of the Covid-19 outbreak. We find that for all sampled countries increase in illiquidity due to temporary shocks reverts to long term trend shortly, suggesting that the liquidity shocks due to the incidence of Covid-19 were short lived. The findings of our VAR analysis show an absence of any short-term relationship between Covid-19 new cases or deaths and illiquidity. Since the series are not integrated at same level, long-term relationship between Covid-19 and stock market illiquidity do not exist as well suggesting no evidence of the effect of Covid-19 on stock market liquidity. [ABSTRACT FROM AUTHOR]

This pragmatic research strives to reveal the return volatility transmission throughout Asian stock exchanges, by employing variance decomposition technique of Vector autoregressive (VAR) based framework. Additionally, the current examination exerts a Granger causality approach to detect short-term cause and effect among the stock exchanges. The consequence of volatility spill-over exhibits the dominancy of Indian, Chinese and Japanese exchanges in terms of net volatility transmitter. Further, it is found that Korean, Thai, and Malaysian stock exchanges seem to be net receiver of volatility in Asia. Additionally, the outcome of current investigation reveals neutrality of Bangladeshi and Pakistani stock exchange, as the returns volatility of these stock exchange are not influenced by any other Asian stock exchanges. Furthermore, the result of Granger causality analysis signifies the existence of unidirectional causality among the Asian stock exchanges. In terms of policy implication, it is imperative for investors and policymakers to closely monitor the behaviour of the Japanese stock exchange, as it plays a significant role as a net transmitter of volatility to other stock exchanges in Asia. By keeping a vigilant eye on the Japanese stock exchange, investors can better assess and manage potential risks and opportunities in the region. [ABSTRACT FROM AUTHOR]

This paper aims to introduce novel systemic risk indicators based on risk allocation methods employed in actuarial science. We present diverse general approaches for constructing these indicators and utilize them to derive indicators based on commonly used risk measures such as Value at Risk, Tail Value at Risk, and Expectiles. Furthermore, we analyze the influence of the dependence structure on the behavior of these indicators using a range of copula models. To support our findings, we provide numerical illustrations. [ABSTRACT FROM AUTHOR]

Copyright of Panorama Económico is the property of Universidad de Cartagena and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper surveys the existing literature on the relationship between inflation targeting (IT) and exchange rate pass-through (ERPT), with a focus on drawing lessons for future IT countries and estimating ERPT in India. The study finds that the main lessons emerging from the experience of IT in reducing pass-through are (a) monetary policy should focus on future inflation, (b) coordination between monetary and fiscal policy is important, and (c) IT is not a panacea. Furthermore, ERPT varies across countries and depends on the composition of the inflation index. The paper also presents a case study of Indian using Vector autoregression model. The study finds decline in ERPT during the IT period.

This study tests the existence and direction of the relationship between investment in intangible assets and export and internationalization growth using the VAR, IRF, and VECM approaches. To gain a deeper understanding of this relationship, the following research question was posed: How does investment in intangible assets affect Croatian exporters’ export and internationalization growth? How long does this effect last? Based on the microfinance data of the largest Croatian exporters, the results suggest a relationship between exports and investments in intangible assets in previous periods. Accumulated impulse responses suggest that investment in intangible assets reduces and increases export growth. This U-shaped relationship between intangible asset investment and export growth does not converge to equilibrium within the ten years studied. The VECM shows that increased intangible assets lead to increased exports in the long run. However, the speed of adjustment to long-run equilibrium is slow, only 6.42% annually. The study is significant in two ways. First, it points out the relationship between investment in intangible assets and long-term export growth. Second, it provides information for policy implementation on the choice of strategic direction companies need to take to reposition themselves, preferably in forward-looking value chains, while opening discussion of the institutional infrastructure needed for this repositioning.

The topic of the paper focuses on the movement of four stock market indices in the period from 2007 to 2022, with the aim of assessing which crisis had a greater impact on individual indices as well as on the portfolio created through mean variance analysis. Also, the risk itself was measured using the VaR (Value at Risk) method. Each index represents one asset class, and computer calculation using mean-variance analysis suggested certain weights, which were used in further calculations. The main conclusion of this paper is that the crisis in 2020 had a stronger impact on all indices, and led to greater volatility in income than the crisis in 2008. Further, a portfolio created through mean analysis for risk-averse investors confirmed these results, showing the highest volatility in the returns of the entire portfolio in 2020.

The main goal of this research is to evaluate the returns and risks of the following types of assets: Bitcoin, EUR Stoxx 50, gold, bonds: government bonds ICE Bof A 1-10 Year excluding Italy and Greece and the corporate bond index ICEB of A 1-10 Year AA. The paper tested a total of ten portfolios according to different scenarios for digital and financial assets. Also, in the paper, greater measures of risk and return were calculated with the aim of forming an optimal portfolio with minimal risk. The results of this research revealed that the correlation between Bitcoin and other forms of financial assets is generally low and negative, which can be a good instrument for portfolio diversification, and positively affect portfolio performance. Also, the results of this study showed that in terms of volatility and return measure of a total of ten portfolios, the second portfolio (whose structure consists of Bitcoin, Euro Stoxx 50, gold, government bonds ICE Bof A 1-10 Year - excluding Italy and Greece and the corporate index bond ICEBof A 1-10 Year AA) is the most optimal portfolio. The findings of this research can serve in risk and loss assessments of portfolio managers, investors, and regulators.

The rapid development of Internet money funds (IMFs) may become the main development direction of money funds in the future. For the characteristics of IMFs return time series data with solid nonlinearity and poor smoothness, this study uses long and short-term memory (LSTM) neural network to predict IMFs return. By constructing a 1DCNN (one-dimensional convolutional neural network) and a self-attentive mechanism, the LSTM feature extraction capability is optimized, and an XGBOOST model is built after the output layer to construct a prediction error sequence to compensate for the original prediction sequence to achieve a correction effect. Finally, the trained model is applied to rolling forecast the 43-day return data of the actual trading days in the next two months, and the VaR method is applied to realize the IMF risk measure. The results displayed the following: (1) The 1DCNN-LSTM-SAM-XG has a significant improvement in accuracy compared with models such as LSTM neural network and SVR, and the MAPE values are reduced by 1.372% and 2.887%, respectively, indicating that the model established in this study is characterized by high accuracy and robustness. (2) According to the VaR methodology, the FUND series has the highest risk, the BANK series has the second highest risk, and the THIRD series has the lowest risk.

This paper studies the effects of a negative economic shock on short-and long-term youth emancipation in Spain over the period 1995-2017. We use a vector autoregressive (VAR) model with different endogenous and exogenous variables which might have an impact on youth residential emancipation according to the academic literature. The results show how emancipation is impacted negatively by the shock after two quarters on average. Following this, the situation returns to its prior state at an accelerated rate. We also find that, in the short-term, the unemployment rate has a greater influence than the temporary employment rate on youth emancipation. In the long-term, this trend is reversed. To conclude, we find that emancipation processes do not depend as much on entry into the labour market as they do on the conditions to stay in it.

The incorporation of renewable energy into the existing electrical system is vital in a competitive electrical system. The unpredictable nature of renewable sources is the main obstacle to energy source integration. Since wind energy is unpredictable, integrating it into an existing thermal system requires some additional operating procedures to maintain the economic and functioning sustainability of the system. In a competitive power network, renewable energy uncertainty creates an imbalance cost (IC) which directly affects the system economy. This study investigates system generation costs, voltage profiles, and electric losses in a deregulated power market incorporating wind farms (WF) & fuel cells (FC). The fuel cell has been used here as a reserve generating unit to mitigate the deficit of power in the renewable incorporated system. To check the efficacy of the presented method, two locations in India are chosen at random. To assess the imbalance cost caused by the discrepancy between forecasted (FWS) and actual wind speeds (AWS), several charge rates (i.e. surplus and deficit) were established. The electrical system has been restructured, so consumers are continually looking for efficient and stable economic power which is only possible by reducing the system risk. This paper outlines a strategy for the optimal operation of a Thyristor-Controlled Series Compensator (TCSC) and fuel cell in a wind-integrated system to maximize system profit and minimize the system risk. In this work, different algorithms like Sequential Quadratic Programming (SQP), Artificial Bee Colony Algorithms (ABC), and Moth Flame Optimization Algorithms (MFO) are used to analyze the economic and functional risk of the system. Additionally, it explains how the fuel cell system is employed to offset the wind farm integration’s deviation in the real-time power market. Value-at-Risk (VaR) and conditional Value-at-Risk (cVaR) have been used for risk analysis. A modified IEEE 14-bus test system is considered to validate the entire study whereas any small, large as well as hybrid systems can be considered to perform this methodology.